What Makes a Data Visualization Memorable?

As data designers, we’re always on the lookout for new ways to present information most effectively. It’s not just about making data look good; it’s about making the experience of viewing that data better for the audience in many ways. One of the most debated topics in data design is whether or not chart junk—all those additional illustrations and design elements meant to enhance aesthetics of data visualizations—are effective at making a chart more memorable or not. Experts like Stephen Few and Edward Tufte are mostly against chart junk, while various studies have produced conflicting results.

An example of chart junk.

To examine the issue, in 2013 MIT ran the largest-scale visualization study yet, delving into what exactly makes a visualization memorable. Specifically, researchers were looking how our brains retain visualizations as images (meaning the study did not focus on whether an aesthetically enhanced visualization was easier to understand or aided comprehension).

To start, researchers culled 2,070 visualization samples from the web, breaking them down into twelve categories according to different visualization type: area; bar; circle; diagram; distribution; grid and matrix; line; map; point; table; text; and trees and networks. Each visualization was also classified according to certain visual attributes informative for memorability, such as data-ink ratio and visual density.

After categorizing all visualization, 410 target visualizations were identified for testing. Researchers then devised a game in which participants were presented with a sequence of images. When participants saw an image for the second time in the sequence, they would press a key, allowing researchers to track the most memorable visualizations.

The results? Yet again, the study both confirmed and refuted various beliefs held by individuals on both sides of the chart junk debate, but one thing was clear: The experiment revealed that visualizations are intrinsically memorable.

Among other notable findings, researchers found that:

A visualization containing a familiar image is more easily recognizable and therefore memorable. This includes “natural”-looking visualizations—those similar to scenes, objects, and people.

A visualization is more memorable if it includes a pictogram or cartoon of a recognizable image. In short, not all chart junk is created equal. Certain graphic elements and visualization types (grid/matrix, trees and networks, diagrams, and visualizations containing pictorial elements) had significantly higher memorability scores than common graphs (circles, area, points, bars, and lines).

A visualization is more memorable if there is more color. Visualizations with 7 or more colors had the highest memorability scores.

Visualizations with low data-to-ink ratios and high visual densities were more memorable. Specifically, visualizations with chart junk and visual “clutter” were more memorable than minimal, clean visualizations.

Naturally, these findings don’t apply to all use-cases, and there are plenty more questions to be answered before we can identify an exact formula for a memorable visualization.

How does context or subject matter affect the perception of visualization?

Which aspects increase the comprehension or retention of data?

How can you balance interesting design with visual overkill?

Although it may be some time before we have more concrete answers, we love to see academia devoting more research to how to best optimize the presentation of information for maximum impact. After all, data design is still an art and a science—we’ll just have to wait for a little more science.